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Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties
Abstract
Introduction The combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) is emerging as a powerful tool for causally investigating cortical mechanisms and networks. However, various artefacts contaminate TMS-EEG recordings, particularly over regions such as the dorsolateral prefrontal cortex (DLPFC). The aim of this study was to substantiate removal of artefacts from TMS-EEG recordings following stimulation of the DLPFC and motor cortex using independent component analysis (ICA). Methods 36 healthy volunteers (30.8 ± 9 years, 9 female) received 75 single TMS pulses to the left DLPFC or left motor cortex while EEG was recorded from 57 electrodes. A subset of 9 volunteers also received 50 sham pulses. The large TMS artefact and early muscle activity (-2 to ~15 ms) were removed using interpolation and the remaining EEG signal was processed in two separate ICA runs using the FastICA algorithm. Five sub-types of TMS-related artefacts were manually identified: remaining muscle artefacts, decay artefacts, blink artefacts, auditory-evoked potentials and other noise-related artefacts. The cause of proposed blink and auditory-evoked potentials was assessed by concatenating known artefacts (i.e. voluntary blinks or auditory-evoked potentials resulting from sham TMS) to the TMS trials before ICA and evaluating grouping of resultant independent components (ICs). Finally, we assessed the effect of removing specific artefact types on TMS-evoked potentials (TEPs) and TMS-evoked oscillations. Results Over DLPFC, ICs from proposed muscle and decay artefacts correlated with TMS-evoked muscle activity size, whereas proposed TMS-evoked blink ICs combined with voluntary blinks and auditory ICs with auditory-evoked potentials from sham TMS. Individual artefact sub-types characteristically distorted each measure of DLPFC function across the scalp. When free of artefact, TEPs and TMS-evoked oscillations could be measured following DLPFC stimulation. Importantly, characteristic TEPs following motor cortex stimulation (N15, P30, N45, P60, N100) could be recovered from artefactual data, corroborating the reliability of ICA-based artefact correction. Conclusions Various different artefacts contaminate TMS-EEG recordings over the DLPFC and motor cortex. However, these artefacts can be removed with apparent minimal impact on neural activity using ICA, allowing the study of TMS-evoked cortical network properties.
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“During TMS-EEG, participants listened to white noise through intra-auricular earphones (Etymotic Research, ER3-14A, USA) to reduce the influence of auditory processing of the TMS click (Rogasch et al., 2014).”
From Investigation of neurobiological responses to theta burst stimulation during recovery from mild traumatic brain injury (mTBI) (2023) · cites this paper as Rogasch et al., 2014
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“Both rounds of component rejection following FastICA utilised a semiautomated artefact detection algorithm, based on previous research (Rogasch et al., 2014) and using TESA toolbox (Rogasch et al., 2017).”
From Investigation of neurobiological responses to theta burst stimulation during recovery from mild traumatic brain injury (mTBI) (2023) · cites this paper as Rogasch et al., 2014
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Valero‐Cabré, A., Amengual, J. L., Stengel, C., Pascual‐Leone, Á., & Coubard, O. A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.10.006 · Cited by 483
“…induce electrical artifacts on the recorded EEG signal hence several TMS artifact cancelation approaches have been proposed (Litvak et al., 2007; Rogasch et al., 2014; Thut et al., 2017; Thut et al., 2005) to clean as much as possible stimulation artifacts from post-pulse EEG evoked and…”
BackgroundRogasch, N. C., Sullivan, C., Thomson, R. H., Rose, N. S., Bailey, N. W., Fitzgerald, P. B., Farzan, F., & Hernandez-Pavon, J. C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.031 · Cited by 411
Self-citationThe non-transcranial TMS-evoked potential is an inherent source of ambiguity in TMS-EEG studies
Conde, V., Tomasevic, L., Akopian, I., Stanek, K., Saturnino, G. B., Thielscher, A., Bergmann, T. O., & Siebner, H. R. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.052 · Cited by 343
“…the amplitudes of the TEPs were well within the range of previous TMS-EEG studies on healthy conscious individuals, ranging from 2 to 6 μV (Herring et al., 2015; Kahkonen et al., 2005; Kerwin et al., 2018; Komssi et al., 2004; Noda et al., 2016; Premoli et al., 2014a; Rogasch et al., 2014).”
ResultBergmann, T. O., Karabanov, A. N., Hartwigsen, G., Thielscher, A., & Siebner, H. R. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.012 · Cited by 359
“Following the interference approach, the coil position and orientation producing themaximally disruptive effects is considered optimal, e.g. when functionally localizing Broca's area by inducing speech arrest (Rogic et al., 2014) or to localize the FEF by saccade delay (Ro et al., 2002).”
TMS combined with EEG: Recommendations and open issues for data collection and analysis
Hernandez-Pavon, J. C., Veniero, D., Bergmann, T. O., Belardinelli, P., Bortoletto, M., Casarotto, S., Casula, E. P., Farzan, F., Fecchio, M., Julkunen, P., et al. (2023) · Brain Stimulation · doi:10.1016/j.brs.2023.02.009 · Cited by 238
“Figure created with BioRender.com. and 180 (P180) milliseconds [28,32,35,36].”
InfluentialSelf-citation
Metadata
- Authors
- Rogasch, N. C., Thomson, R. H., Farzan, F., Fitzgibbon, B. M., Bailey, N. W., Hernandez-Pavon, J. C., Daskalakis, Z. J., & Fitzgerald, P. B.
- Affiliations
- The Alfred Hospital (AU)
- Centre for Addiction and Mental Health (CA)
- Aalto University (FI)
- Corresponding author
- Rogasch, Nigel C.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 101
- Pages
- 425-439
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1969040087
- Cited by
- 309
- References
- 77
- Citations by year
- 2026: 34 · 2025: 28 · 2024: 25 · 2023: 33 · 2022: 36 · 2021: 34 · 2020: 18 · 2019: 26 · 2018: 26 · 2017: 22 · 2016: 19 · 2015: 7 · 2014: 1
- FWCI
- 7.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Funders
- Pfizer
- BrainsWay
- National Health and Medical Research Council
- National Medical Research Council
- Topics
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
- Keywords
- TMS-EEG, artifact removal, independent component analysis, TMS-evoked potentials, dorsolateral prefrontal cortex, motor cortex, auditory evoked potentials, FastICA, EEG signal processing
- MeSH terms
- Adult, Data Interpretation, Statistical, Electroencephalography, Evoked Potentials, Female, Humans, Male, Motor Cortex, Artifacts, Prefrontal Cortex, Transcranial Magnetic Stimulation, Young Adult
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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